A personalized multitasking framework for real-time prediction of blood glucose levels in type 1 diabetes patients.

Real-time prediction of blood glucose levels (BGLs) in individuals with type 1 diabetes (T1D) presents considerable challenges. Accordingly, we present a personalized multitasking framework aimed to forecast blood glucose levels in patients. The patient data was initially categorized according to gender and age and subsequently utilized as input for a modified GRU network model, creating five prediction sub-models. The model hyperparameters were optimized and tuned after introducing the decay factor and incorporating the TCN network and attention mechanism into the GRU model. This step was undertaken to improve the capability of feature extraction. The Ohio T1DM clinical dataset was used to train and evaluate the performance of the proposed model. The metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Clark Error Grid Analysis (EGA), were used to evaluate the performance. The results showed that the average RMSE and the MAE of the proposed model were 16.896 and 9.978 mg/dL, respectively, over the prediction horizon (PH) of 30 minutes. The average RMSE and the MAE were 28.881 and 19.347 mg/dL, respectively, over the PH of 60 min. The proposed model demonstrated excellent prediction accuracy. In addition, the EGA analysis showed that the proposed model accurately predicted 30-minute and 60-minute PH within zones A and B, demonstrating that the framework is clinically feasible. The proposed personalized multitask prediction model in this study offers robust assistance for clinical decision-making, playing a pivotal role in improving the outcomes of individuals with diabetes.

AWD-stacking: An enhanced ensemble learning model for predicting glucose levels.

Accurate prediction of blood glucose levels is essential for type 1 diabetes optimizing insulin therapy and minimizing complications in patients with type 1 diabetes. Using ensemble learning algorithms is a promising approach. In this regard, this study proposes an improved stacking ensemble learning algorithm for predicting blood glucose level, in which three improved long short-term memory network models are used as the base model, and an improved nearest neighbor propagation clustering algorithm is adaptively weighted to this ensemble model. The OhioT1DM dataset is used to train and evaluate the performance of the proposed model. This study evaluated the performance of the proposed model using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Matthews Correlation Coefficient (MCC) as the evaluation metrics. The experimental results demonstrate that the proposed model achieves an RMSE of 1.425 mg/dL, MAE of 0.721 mg/dL, and MCC of 0.982 mg/dL for a 30-minute prediction horizon(PH), RMSE of 3.212 mg/dL, MAE of 1.605 mg/dL, and MCC of 0.950 mg/dL for a 45-minute PH; and RMSE of 6.346 mg/dL, MAE of 3.232 mg/dL, and MCC of 0.930 mg/dL for a 60-minute PH. Compared with the best non-ensemble model StackLSTM, the RMSE and MAE were improved by up to 27.92% and 65.32%, respectively. Clarke Error Grid Analysis and critical difference diagram revealed that the model errors were within 10%. The model proposed in this study exhibits state-of-the-art predictive performance, making it suitable for clinical decision-making and of significant importance for the effective treatment of diabetes in patients.

Alcohol degradation, learning, and memory-enhancing effect of Acetobacter pasteurianus BP2201 in Caenorhabditis elegans model.

AIMS: This study aimed to investigate the probiotic effects of Acetobacter pasteurianus BP2201, isolated from brewing mass, for the treatment of alcohol-induced learning and memory ability impairments in a Caenorhabditis elegans model. METHODS AND RESULTS: Acetobacter pasteurianus BP2201 was examined for probiotic properties, including acid and bile salt resistance, ethanol degradation, antioxidant efficacy, hemolytic activity, and susceptibility to antibiotics. The strain displayed robust acid and bile salt tolerance, efficient ethanol degradation, potent antioxidant activity, and susceptibility to specific antibiotics. Additionally, in the C. elegans model, administering A. pasteurianus BP2201 significantly improved alcohol-induced learning and memory impairments. CONCLUSIONS: Acetobacter pasteurianus BP2201 proves to be a promising candidate strain for the treatment of learning and memory impairments induced by alcohol intake.

A Generic Graph-Based Neural Architecture Encoding Scheme With Multifaceted Information.

Neural architecture search (NAS) can automatically discover well-performing architectures in a large search space and has been shown to bring improvements to various applications. However, the computational burden of NAS is huge, since exploring a large search space can need evaluating more than thousands of architecture samples. To improve the sample efficiency of search space exploration, predictor-based NAS methods learn a performance predictor of architectures, and utilize the predictor to sample worth-evaluating architectures. The encoding scheme of NN architectures is crucial to the predictor's generalization ability, and thus crucial to the efficacy of the NAS process. To this end, we have designed a generic Graph-based neural ArchiTecture Encoding Scheme (GATES), a more reasonable modeling of NN architectures that mimics their data processing. Nevertheless, GATES is unaware of the concrete computing semantic of NN operations or architectures. Thus, the learning of operation embeddings and weights in GATES can only exploit the information in architectures-performance pairs. We propose GATES++, which incorporates multifaceted information about NN's operation-level and architecture-level computing semantics into its construction and training, respectively. Experiments on benchmark search spaces show that both the operation-level and architecture-level information can bring improvements alone, and GATES++ can discover better architectures after evaluating the same number of architectures.

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead resting electrocardiogram dataset with 15,357 records, and called for a community effort to improve the performances of CIE through the China ECG AI Contest 2019. This dataset covers most types of ECG interpretations, including the normal type, 8 common abnormal types, and the other type which includes both uncommon abnormal and noise signals. Based on the Contest, we systematically assessed and analyzed a set of top-performing methods, most of which are deep neural networks, with both their commonalities and characteristics. This study establishes the benchmarks for computerized interpretation of 12-lead resting electrocardiogram and provides insights for the development of new methods. Graphical Abstract A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Unsupervised Domain Adaptation for ECG Arrhythmia Classification.

Electrocardiograph (ECG) is one of the most critical physiological signals for arrhythmia diagnosis in clinical practice. In recent years, various algorithms based on deep learning have been proposed to solve the heartbeat classification problem and achieved saturated accuracy in intrapatient paradigm, but encountered performance degradation in inter-patient paradigm due to the drastic variation of ECG signals among different individuals. In this paper, we propose a novel unsupervised domain adaptation scheme to address this problem. Specifically, we first propose a robust baseline model called Multi-path Atrous Convolutional Network (MACN) to tackle ECG heartbeat classification. Further, we introduce Cluster-aligning loss and Cluster-separating loss to align the distributions of training and test data and increase the discriminability, respectively. The proposed method requires no expert annotations but a short period of unlabelled data in new records. Experimental results on the MIT-BIH database demonstrate that our scheme effectively intensifies the baseline model and achieves competitive performance with other state-of-the-arts.

The Role and Challenges of Body Channel Communication in Wearable Flexible Electronics.

Flexible electronics are compatible with film substrates that are soft and stretchable, resulting in conformal integration with human body. Integrated with various sensors and communication ICs, wearable flexible electronics are able to effectively track human vital signs without affecting the body activities. Such a wearable flexible system contains a sensor, a front-end amplifier (FEA), an analog-to-digital converter (ADC), a micro-controller unit (MCU), a radio, a power management unit (PMU), where the radio is the design bottleneck due to its high power consumption. Different from conventional wireless communications, body channel communication (BCC) uses the human body surface as the signal transmission medium resulting in less signal loss and low power consumption. However, there are some design challenges in BCC, including body channel model, backward loss, variable contact impedance, stringent spectral mask, crystalless design, body antenna effect, etc. In this paper, we conduct a survey on BCC transceiver, and analyze its potential role and challenges in wearable flexible electronics.

Investigation and Modeling of Multi-Node Body Channel Wireless Power Transfer.

Insufficient power supply is a huge challenge for wireless body area network (WBAN). Body channel wireless power transfer (BC-WPT) is promising to realize multi-node high-efficiency power transmission for miniaturized WBAN nodes. However, the behavior of BC-WPT, especially in the multi-node scenario, is still lacking in research. In this paper, the inter-degeneration mechanism of a multi-node BC-WPT is investigated based on the intuitive analysis of the existing circuit model. Co-simulation in the Computer Simulation Technology (CST) and Cadence platform and experiments in a general indoor environment verify this mechanism. Three key factors, including the distance between the source and the harvester, frequency of the source, and area of the ground electrodes, are taken into consideration, resulting in 15 representative cases for simulation and experiments studies. Based on the simulation parameters, an empirical circuit model to accurately predict the received power of multiple harvesters is established, which fits well with the measurement results, and can further provide guidelines for designs and research on multi-node BC-WPT systems.

Large depth-of-field 3D shape measurement using an electrically tunable lens.

The state-of-the-art 3D shape measurement system has rather shallow working volume due to the limited depth-of-field (DOF) of conventional lens. In this paper, we propose to use the electrically tunable lens to substantially enlarge the DOF. Specifically, we capture always in-focus phase-shifted fringe patterns by precisely synchronizing the tunable lens attached to the camera with the image acquisition and the pattern projection; we develop a phase unwrapping framework that fully utilizes the geometric constraint from the camera focal length setting; and we pre-calibrate the system under different focal distance to reconstruct 3D shape from unwrapped phase map. To validate the proposed idea, we developed a prototype system that can perform high-quality measurement for the depth range of approximately 1,000 mm (400 mm - 1400 mm) with the measurement error of 0.05%. Furthermore, we demonstrated that such a technique can be used for real-time 3D shape measurement by experimentally measuring moving objects.

Guided, Fusion-Based, Large Depth-of-field 3D Imaging Using a Focal Stack.

Three dimensional (3D) imaging technology has been widely used for many applications, such as human-computer interactions, making industrial measurements, and dealing with cultural relics. However, existing active methods often require both large apertures of projector and camera to maximize light throughput, resulting in a shallow working volume in which projector and camera are simultaneously in focus. In this paper, we propose a novel method to extend the working range of the structured light 3D imaging system based on the focal stack. Specifically in the case of large depth variation scenes, we first adopted the gray code method for local, 3D shape measurement with multiple focal distance settings. Then we extracted the texture map of each focus position into a focal stack to generate a global coarse depth map. Under the guidance of the global coarse depth map, the high-quality 3D shape measurement of the overall scene was obtained by local, 3D shape-measurement fusion. To validate the method, we developed a prototype system that can perform high-quality measurements in the depth range of 400 mm with a measurement error of 0.08%.

An Auto Loss Compensation System for Capacitive-Coupled Body Channel Communication.

This paper proposes an auto loss compensation (ALC) system to attenuate the time-variant path loss for capacitive-coupled body channel communication (CC-BCC). The system employs a time-division gradient indicator to continuously monitor the compensation conditions, and dynamically adjust the compensation inductor through a proportional integral (PI) controller. With the closed-loop topology, the proposed ALC system has two major advantages: first, the path loss induced by the backward coupling effect can be compensated without calibration; second, this system can dynamically attenuate the path loss, even when the channel characteristics vary with time. The simulation and experimental results show that the proposed ALC system can significantly attenuate the backward path loss, especially under wearable and motion scenarios.

Dynamic Channel Modeling and OFDM System Analysis for Capacitive Coupling Body Channel Communication.

Body channel communication (BCC) has the potential to achieve better energy efficiency over other conventional wireless communication schemes, thus becomes a promising solution for the wireless body area network. To deal with the fading and dynamic variation challenges of BCC, the technique of orthogonal frequency-division multiplexing (OFDM) is a promising candidate. However, some basic issues in OFDM including the pilot design and the modulating methods have not been analyzed for BCC. The contribution of this paper includes proposing a dynamic channel model of BCC for system level designing, analyzing the pilot design method, and proposing an adaptive modulating algorithm for BCC. Practical communication experiments based on software define radio are also implemented to validate the the effectiveness of the pilot design method and the modulating algorithm.

Channel Loss in Contactless Human Body Communication.

Human body communication (HBC) utilizes human body as the transmission medium to facilitate data communications in a wireless body area network (WBAN). It normally uses a pair of transmitting (Tx) and receiving (Rx) electrodes clinging to the body surface to form a low-loss body channel, so a higher energy efficiency can be achieved in comparison to conventional wireless communications. In HBC, the Tx electrode can be shared with vital sign monitoring electrode, such as ECG electrode or EEG electrode, to inject the signal into body. As for the Rx electrode, it can be either in direct contact to body surface or placed in proximity to body surface. The late case forms a contactless HBC communication, which find more applications in the WBAN, e.g. a smart phone in one's pocket to receive ECG signal from the chest electrode. In view of the adverse effect caused by the contactless case, this paper presents a study on the path loss of contactless HBC, which are investigated by finite element method (FEM) and verified by actual measurements. An empirical formula for path loss and contactless space is derived, showing that the path loss is increased by 18 dB when the distance between electrode and body increases from 1 mm to 10 mm. It also shows a 5 dB reduction on path loss with a 50% increase of the electrode size.

Real-Time ECG Delineation with Randomly Selected Wavelet Transform Feature and Random Walk Estimation.

Detection of Electrocardiogram (ECG) characteristic points can provide critical diagnostic information about heart diseases. We propose a novel feature extraction and machine learning scheme for ECG delineation. A new feature, termed as randomly selected wavelet transform (RSWT), is proposed to effectively represent ECG morphology. With the RSWT feature pool, a regression tree is trained to estimate the probability distribution to the direction toward the target point, relative to the current position. The continual random walk through 1D space will eventually produce a reliable region from which the final position of the target point is derived. The evaluation results on QT database show better detection accuracy compared with other studies while providing real-time processing capability.

Bidirectional Recurrent Neural Network And Convolutional Neural Network (BiRCNN) For ECG Beat Classification.

We propose a novel electrocardiogram (ECG) beat classification algorithm using a combination of Bidirectional Recurrent Neural Network (BiRNN) and Convolutional Neural Network (CNN) named as BiRCNN. Our model is an end-to-end model. The morphological features of each ECG beat is extracted by CNN. Then the features of each beat are considered in the context via BiRNN. The assessment on MIT-BIH Arrhythmia Database (MITDB) resulted in a sensitivity of 98.7% and a positive predictivity of 96.4% on average for the VEB class. For the SVEB class, the sensitivity was 92.8%, which was an over 6% promotion compared with the state-of-the-art method, and the positive predictivity was 81.9% on average. The results demonstrate the superior classification performance of our method.

Region Aggregation Network: Improving Convolutional Neural Network for ECG Characteristic Detection.

Detection of ECG characteristic points serves as the first step in automated ECG analysis techniques. We propose a novel end-to-end deep learning scheme called Region Aggregation Network (RAN) for ECG characteristic points de- tection. A 1D Convolutional Neural Network (CNN) is adopted to automatically process ECG signals. A novel strategy of Region Aggregation is proposed to replace the conventional fully connected layer as regressor. Our work provides robust and accurate detection performance on public ECG database. The evaluation results of our method on QT database show comparable detection accuracy compared with state-of-the-art works.

A Five-Tissue-Layer Human Body Communication Circuit Model Tunable to Individual Characteristics.

Human body communication (HBC) has several advantages over traditional wireless communications due to the high conductivity of human body. An accurate body channel model plays a vital role in optimizing the performance and power of HBC transceivers. In this paper, we present a body channel model with three distinct features. First, it takes into account all five body tissue layers resulting better accuracy; second, it adapts to different individuals with the proposed layer thickness estimation technique; third, it counts in the variation of backward coupling capacitance versus different postures. These new features significantly improve the model accuracy. Measurement results show that the proposed model achieves a maximum error of 2.21% in path loss for different human subjects.

PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data.

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease.

A Self-Adaptive Capacitive Compensation Technique for Body Channel Communication.

In wireless body area network, capacitive-coupling body channel communication (CC-BCC) has the potential to attain better energy efficiency over conventional wireless communication schemes. The CC-BCC scheme utilizes the human body as the forward signal transmission medium, reducing the path loss in wireless body-centric communications. However, the backward path is formed by the coupling capacitance between the ground electrodes (GEs) of transmitter (Tx) and receiver (Rx), which increases the path loss and results in a body posture dependent backward impedance. Conventional methods use a fixed inductor to resonate with the backward capacitor to compensate the path loss, while it's not effective in compensating the variable backward impedance induced by the body movements. In this paper, we propose a self-adaptive capacitive compensation (SACC) technique to address such a problem. A backward distance detector is introduced to estimate the distance between two GEs of Tx and Rx, and a backward capacitance model is built to calculate the backward capacitance. The calculated backward capacitance at varying body posture is compensated by a digitally controlled tunable inductor (DCTI). The proposed SACC technique is validated by a prototype CC-BCC system, and measurements are taken on human subjects. The measurement results show that 9dB-16 dB channel enhancement can be achieved at a backward path distance of 1 cm-10 cm.

An Investigation on Ground Electrodes of Capacitive Coupling Human Body Communication.

Utilizing the body surface as the signal transmission medium, capacitive coupling human body communication (CC-HBC) can achieve a much higher energy efficiency than conventional wireless communications in future wireless body area network (WBAN) applications. Under the CC-HBC scheme, the body surface serves as the forward signal path, whereas the backward path is formed by the capacitive coupling between the ground electrodes (GEs) of transmitter (TX) and receiver (RX). So the type of communication benefits from a low forward loss, while the backward loss depending on the GE coupling strength dominates the total transmission loss. However, none of the previous works have shown a complete research on the effects of GEs. In this paper, all kinds of GE effects on CC-HBC are investigated by both finite element method (FEM) analysis and human body measurement. We set the TX GE and RX GE at different heights, separation distances, and dimensions to study the corresponding influence on the overall signal transmission path loss. In addition, we also investigate the effects of GEs with different shapes and different TX-to-RX relative angles. Based on all the investigations, an analytical model is derived to evaluate the GE related variations of channel loss in CC-HBC.